Deep learning-assisted PET imaging achieves fast scan/low-dose examination

Purpose This study aimed to investigate the impact of a deep learning (DL)-based denoising method on the image quality and lesion detectability of 18F-FDG positron emission tomography (PET) images. Methods Fifty-two oncological patients undergoing an 18F-FDG PET/CT imaging with an acquisition of 180 s per bed position were retrospectively included. The list-mode data were rebinned into four datasets: 100% (reference), 75%, 50%, and 33.3% of the total counts, and then reconstructed by OSEM algorithm and post-processed with the DL and Gaussian filter (GS). The image quality was assessed using a 5-point Likert scale, and FDG-avid lesions were counted to measure lesion detectability. Standardized uptake values (SUVs) in livers and lesions, liver signal-to-noise ratio (SNR) and target-to-background ratio (TBR) values were compared between the methods. Subgroup analyses compared TBRs after categorizing lesions based on parameters like lesion diameter, uptake or patient habitus. Results The DL method showed superior performance regarding image noise and inferior performance regarding lesion contrast in the qualitative assessment. More than 96.8% of the lesions were successfully identified in DL images. Excellent agreements on SUV in livers and lesions were found. The DL method significantly improved the liver SNR for count reduction down to 33.3% (p < 0.001). Lesion TBR was not significantly different between DL and reference images of the 75% dataset; furthermore, there was no significant difference either for lesions of > 10 mm or lesions in BMIs of > 25. For the 50% dataset, there was no significant difference between DL and reference images for TBR of lesion with > 15 mm or higher uptake than liver. Conclusions The developed DL method improved both liver SNR and lesion TBR indicating better image quality and lesion conspicuousness compared to GS method. Compared with the reference, it showed non-inferior image quality with reduced counts by 25–50% under various conditions. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Xing, Yan [verfasserIn] Qiao, Wenli Wang, Taisong Wang, Ying Li, Chenwei Lv, Yang Xi, Chen Liao, Shu Qian, Zheng Zhao, Jinhua

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Positron emission tomography and computed tomography (PET/CT) Deep learning Denoising technique Image quality

Anmerkung:	© The Author(s) 2022

Übergeordnetes Werk:	Enthalten in: EJNMMI Physics - Berlin : SpringerOpen, 2014, 9(2022), 1 vom: 04. Feb.
Übergeordnetes Werk:	volume:9 ; year:2022 ; number:1 ; day:04 ; month:02

Links:	Volltext

DOI / URN:	10.1186/s40658-022-00431-9

Katalog-ID:	SPR046158901

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allfields	10.1186/s40658-022-00431-9 doi (DE-627)SPR046158901 (SPR)s40658-022-00431-9-e DE-627 ger DE-627 rakwb eng Xing, Yan verfasserin aut Deep learning-assisted PET imaging achieves fast scan/low-dose examination 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Purpose This study aimed to investigate the impact of a deep learning (DL)-based denoising method on the image quality and lesion detectability of 18F-FDG positron emission tomography (PET) images. Methods Fifty-two oncological patients undergoing an 18F-FDG PET/CT imaging with an acquisition of 180 s per bed position were retrospectively included. The list-mode data were rebinned into four datasets: 100% (reference), 75%, 50%, and 33.3% of the total counts, and then reconstructed by OSEM algorithm and post-processed with the DL and Gaussian filter (GS). The image quality was assessed using a 5-point Likert scale, and FDG-avid lesions were counted to measure lesion detectability. Standardized uptake values (SUVs) in livers and lesions, liver signal-to-noise ratio (SNR) and target-to-background ratio (TBR) values were compared between the methods. Subgroup analyses compared TBRs after categorizing lesions based on parameters like lesion diameter, uptake or patient habitus. Results The DL method showed superior performance regarding image noise and inferior performance regarding lesion contrast in the qualitative assessment. More than 96.8% of the lesions were successfully identified in DL images. Excellent agreements on SUV in livers and lesions were found. The DL method significantly improved the liver SNR for count reduction down to 33.3% (p < 0.001). Lesion TBR was not significantly different between DL and reference images of the 75% dataset; furthermore, there was no significant difference either for lesions of > 10 mm or lesions in BMIs of > 25. For the 50% dataset, there was no significant difference between DL and reference images for TBR of lesion with > 15 mm or higher uptake than liver. Conclusions The developed DL method improved both liver SNR and lesion TBR indicating better image quality and lesion conspicuousness compared to GS method. Compared with the reference, it showed non-inferior image quality with reduced counts by 25–50% under various conditions. Positron emission tomography and computed tomography (PET/CT) (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Denoising technique (dpeaa)DE-He213 Image quality (dpeaa)DE-He213 Qiao, Wenli aut Wang, Taisong aut Wang, Ying aut Li, Chenwei aut Lv, Yang aut Xi, Chen aut Liao, Shu aut Qian, Zheng aut Zhao, Jinhua (orcid)0000-0002-8867-7985 aut Enthalten in EJNMMI Physics Berlin : SpringerOpen, 2014 9(2022), 1 vom: 04. Feb. (DE-627)785697993 (DE-600)2768912-8 2197-7364 nnns volume:9 year:2022 number:1 day:04 month:02 https://dx.doi.org/10.1186/s40658-022-00431-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_2446 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2022 1 04 02
spelling	10.1186/s40658-022-00431-9 doi (DE-627)SPR046158901 (SPR)s40658-022-00431-9-e DE-627 ger DE-627 rakwb eng Xing, Yan verfasserin aut Deep learning-assisted PET imaging achieves fast scan/low-dose examination 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Purpose This study aimed to investigate the impact of a deep learning (DL)-based denoising method on the image quality and lesion detectability of 18F-FDG positron emission tomography (PET) images. Methods Fifty-two oncological patients undergoing an 18F-FDG PET/CT imaging with an acquisition of 180 s per bed position were retrospectively included. The list-mode data were rebinned into four datasets: 100% (reference), 75%, 50%, and 33.3% of the total counts, and then reconstructed by OSEM algorithm and post-processed with the DL and Gaussian filter (GS). The image quality was assessed using a 5-point Likert scale, and FDG-avid lesions were counted to measure lesion detectability. Standardized uptake values (SUVs) in livers and lesions, liver signal-to-noise ratio (SNR) and target-to-background ratio (TBR) values were compared between the methods. Subgroup analyses compared TBRs after categorizing lesions based on parameters like lesion diameter, uptake or patient habitus. Results The DL method showed superior performance regarding image noise and inferior performance regarding lesion contrast in the qualitative assessment. More than 96.8% of the lesions were successfully identified in DL images. Excellent agreements on SUV in livers and lesions were found. The DL method significantly improved the liver SNR for count reduction down to 33.3% (p < 0.001). Lesion TBR was not significantly different between DL and reference images of the 75% dataset; furthermore, there was no significant difference either for lesions of > 10 mm or lesions in BMIs of > 25. For the 50% dataset, there was no significant difference between DL and reference images for TBR of lesion with > 15 mm or higher uptake than liver. Conclusions The developed DL method improved both liver SNR and lesion TBR indicating better image quality and lesion conspicuousness compared to GS method. Compared with the reference, it showed non-inferior image quality with reduced counts by 25–50% under various conditions. Positron emission tomography and computed tomography (PET/CT) (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Denoising technique (dpeaa)DE-He213 Image quality (dpeaa)DE-He213 Qiao, Wenli aut Wang, Taisong aut Wang, Ying aut Li, Chenwei aut Lv, Yang aut Xi, Chen aut Liao, Shu aut Qian, Zheng aut Zhao, Jinhua (orcid)0000-0002-8867-7985 aut Enthalten in EJNMMI Physics Berlin : SpringerOpen, 2014 9(2022), 1 vom: 04. Feb. (DE-627)785697993 (DE-600)2768912-8 2197-7364 nnns volume:9 year:2022 number:1 day:04 month:02 https://dx.doi.org/10.1186/s40658-022-00431-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_2446 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2022 1 04 02
allfields_unstemmed	10.1186/s40658-022-00431-9 doi (DE-627)SPR046158901 (SPR)s40658-022-00431-9-e DE-627 ger DE-627 rakwb eng Xing, Yan verfasserin aut Deep learning-assisted PET imaging achieves fast scan/low-dose examination 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Purpose This study aimed to investigate the impact of a deep learning (DL)-based denoising method on the image quality and lesion detectability of 18F-FDG positron emission tomography (PET) images. Methods Fifty-two oncological patients undergoing an 18F-FDG PET/CT imaging with an acquisition of 180 s per bed position were retrospectively included. The list-mode data were rebinned into four datasets: 100% (reference), 75%, 50%, and 33.3% of the total counts, and then reconstructed by OSEM algorithm and post-processed with the DL and Gaussian filter (GS). The image quality was assessed using a 5-point Likert scale, and FDG-avid lesions were counted to measure lesion detectability. Standardized uptake values (SUVs) in livers and lesions, liver signal-to-noise ratio (SNR) and target-to-background ratio (TBR) values were compared between the methods. Subgroup analyses compared TBRs after categorizing lesions based on parameters like lesion diameter, uptake or patient habitus. Results The DL method showed superior performance regarding image noise and inferior performance regarding lesion contrast in the qualitative assessment. More than 96.8% of the lesions were successfully identified in DL images. Excellent agreements on SUV in livers and lesions were found. The DL method significantly improved the liver SNR for count reduction down to 33.3% (p < 0.001). Lesion TBR was not significantly different between DL and reference images of the 75% dataset; furthermore, there was no significant difference either for lesions of > 10 mm or lesions in BMIs of > 25. For the 50% dataset, there was no significant difference between DL and reference images for TBR of lesion with > 15 mm or higher uptake than liver. Conclusions The developed DL method improved both liver SNR and lesion TBR indicating better image quality and lesion conspicuousness compared to GS method. Compared with the reference, it showed non-inferior image quality with reduced counts by 25–50% under various conditions. Positron emission tomography and computed tomography (PET/CT) (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Denoising technique (dpeaa)DE-He213 Image quality (dpeaa)DE-He213 Qiao, Wenli aut Wang, Taisong aut Wang, Ying aut Li, Chenwei aut Lv, Yang aut Xi, Chen aut Liao, Shu aut Qian, Zheng aut Zhao, Jinhua (orcid)0000-0002-8867-7985 aut Enthalten in EJNMMI Physics Berlin : SpringerOpen, 2014 9(2022), 1 vom: 04. Feb. (DE-627)785697993 (DE-600)2768912-8 2197-7364 nnns volume:9 year:2022 number:1 day:04 month:02 https://dx.doi.org/10.1186/s40658-022-00431-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_2446 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2022 1 04 02
allfieldsGer	10.1186/s40658-022-00431-9 doi (DE-627)SPR046158901 (SPR)s40658-022-00431-9-e DE-627 ger DE-627 rakwb eng Xing, Yan verfasserin aut Deep learning-assisted PET imaging achieves fast scan/low-dose examination 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Purpose This study aimed to investigate the impact of a deep learning (DL)-based denoising method on the image quality and lesion detectability of 18F-FDG positron emission tomography (PET) images. Methods Fifty-two oncological patients undergoing an 18F-FDG PET/CT imaging with an acquisition of 180 s per bed position were retrospectively included. The list-mode data were rebinned into four datasets: 100% (reference), 75%, 50%, and 33.3% of the total counts, and then reconstructed by OSEM algorithm and post-processed with the DL and Gaussian filter (GS). The image quality was assessed using a 5-point Likert scale, and FDG-avid lesions were counted to measure lesion detectability. Standardized uptake values (SUVs) in livers and lesions, liver signal-to-noise ratio (SNR) and target-to-background ratio (TBR) values were compared between the methods. Subgroup analyses compared TBRs after categorizing lesions based on parameters like lesion diameter, uptake or patient habitus. Results The DL method showed superior performance regarding image noise and inferior performance regarding lesion contrast in the qualitative assessment. More than 96.8% of the lesions were successfully identified in DL images. Excellent agreements on SUV in livers and lesions were found. The DL method significantly improved the liver SNR for count reduction down to 33.3% (p < 0.001). Lesion TBR was not significantly different between DL and reference images of the 75% dataset; furthermore, there was no significant difference either for lesions of > 10 mm or lesions in BMIs of > 25. For the 50% dataset, there was no significant difference between DL and reference images for TBR of lesion with > 15 mm or higher uptake than liver. Conclusions The developed DL method improved both liver SNR and lesion TBR indicating better image quality and lesion conspicuousness compared to GS method. Compared with the reference, it showed non-inferior image quality with reduced counts by 25–50% under various conditions. Positron emission tomography and computed tomography (PET/CT) (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Denoising technique (dpeaa)DE-He213 Image quality (dpeaa)DE-He213 Qiao, Wenli aut Wang, Taisong aut Wang, Ying aut Li, Chenwei aut Lv, Yang aut Xi, Chen aut Liao, Shu aut Qian, Zheng aut Zhao, Jinhua (orcid)0000-0002-8867-7985 aut Enthalten in EJNMMI Physics Berlin : SpringerOpen, 2014 9(2022), 1 vom: 04. Feb. (DE-627)785697993 (DE-600)2768912-8 2197-7364 nnns volume:9 year:2022 number:1 day:04 month:02 https://dx.doi.org/10.1186/s40658-022-00431-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_2446 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2022 1 04 02
allfieldsSound	10.1186/s40658-022-00431-9 doi (DE-627)SPR046158901 (SPR)s40658-022-00431-9-e DE-627 ger DE-627 rakwb eng Xing, Yan verfasserin aut Deep learning-assisted PET imaging achieves fast scan/low-dose examination 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Purpose This study aimed to investigate the impact of a deep learning (DL)-based denoising method on the image quality and lesion detectability of 18F-FDG positron emission tomography (PET) images. Methods Fifty-two oncological patients undergoing an 18F-FDG PET/CT imaging with an acquisition of 180 s per bed position were retrospectively included. The list-mode data were rebinned into four datasets: 100% (reference), 75%, 50%, and 33.3% of the total counts, and then reconstructed by OSEM algorithm and post-processed with the DL and Gaussian filter (GS). The image quality was assessed using a 5-point Likert scale, and FDG-avid lesions were counted to measure lesion detectability. Standardized uptake values (SUVs) in livers and lesions, liver signal-to-noise ratio (SNR) and target-to-background ratio (TBR) values were compared between the methods. Subgroup analyses compared TBRs after categorizing lesions based on parameters like lesion diameter, uptake or patient habitus. Results The DL method showed superior performance regarding image noise and inferior performance regarding lesion contrast in the qualitative assessment. More than 96.8% of the lesions were successfully identified in DL images. Excellent agreements on SUV in livers and lesions were found. The DL method significantly improved the liver SNR for count reduction down to 33.3% (p < 0.001). Lesion TBR was not significantly different between DL and reference images of the 75% dataset; furthermore, there was no significant difference either for lesions of > 10 mm or lesions in BMIs of > 25. For the 50% dataset, there was no significant difference between DL and reference images for TBR of lesion with > 15 mm or higher uptake than liver. Conclusions The developed DL method improved both liver SNR and lesion TBR indicating better image quality and lesion conspicuousness compared to GS method. Compared with the reference, it showed non-inferior image quality with reduced counts by 25–50% under various conditions. Positron emission tomography and computed tomography (PET/CT) (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Denoising technique (dpeaa)DE-He213 Image quality (dpeaa)DE-He213 Qiao, Wenli aut Wang, Taisong aut Wang, Ying aut Li, Chenwei aut Lv, Yang aut Xi, Chen aut Liao, Shu aut Qian, Zheng aut Zhao, Jinhua (orcid)0000-0002-8867-7985 aut Enthalten in EJNMMI Physics Berlin : SpringerOpen, 2014 9(2022), 1 vom: 04. Feb. (DE-627)785697993 (DE-600)2768912-8 2197-7364 nnns volume:9 year:2022 number:1 day:04 month:02 https://dx.doi.org/10.1186/s40658-022-00431-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_2446 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2022 1 04 02
language	English
source	Enthalten in EJNMMI Physics 9(2022), 1 vom: 04. Feb. volume:9 year:2022 number:1 day:04 month:02
sourceStr	Enthalten in EJNMMI Physics 9(2022), 1 vom: 04. Feb. volume:9 year:2022 number:1 day:04 month:02
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Positron emission tomography and computed tomography (PET/CT) Deep learning Denoising technique Image quality
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container_title	EJNMMI Physics
authorswithroles_txt_mv	Xing, Yan @@aut@@ Qiao, Wenli @@aut@@ Wang, Taisong @@aut@@ Wang, Ying @@aut@@ Li, Chenwei @@aut@@ Lv, Yang @@aut@@ Xi, Chen @@aut@@ Liao, Shu @@aut@@ Qian, Zheng @@aut@@ Zhao, Jinhua @@aut@@
publishDateDaySort_date	2022-02-04T00:00:00Z
hierarchy_top_id	785697993
id	SPR046158901
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author	Xing, Yan
spellingShingle	Xing, Yan misc Positron emission tomography and computed tomography (PET/CT) misc Deep learning misc Denoising technique misc Image quality Deep learning-assisted PET imaging achieves fast scan/low-dose examination
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topic_title	Deep learning-assisted PET imaging achieves fast scan/low-dose examination Positron emission tomography and computed tomography (PET/CT) (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Denoising technique (dpeaa)DE-He213 Image quality (dpeaa)DE-He213
topic	misc Positron emission tomography and computed tomography (PET/CT) misc Deep learning misc Denoising technique misc Image quality
topic_unstemmed	misc Positron emission tomography and computed tomography (PET/CT) misc Deep learning misc Denoising technique misc Image quality
topic_browse	misc Positron emission tomography and computed tomography (PET/CT) misc Deep learning misc Denoising technique misc Image quality
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
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title	Deep learning-assisted PET imaging achieves fast scan/low-dose examination
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title_full	Deep learning-assisted PET imaging achieves fast scan/low-dose examination
author_sort	Xing, Yan
journal	EJNMMI Physics
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author_browse	Xing, Yan Qiao, Wenli Wang, Taisong Wang, Ying Li, Chenwei Lv, Yang Xi, Chen Liao, Shu Qian, Zheng Zhao, Jinhua
container_volume	9
format_se	Elektronische Aufsätze
author-letter	Xing, Yan
doi_str_mv	10.1186/s40658-022-00431-9
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title_sort	deep learning-assisted pet imaging achieves fast scan/low-dose examination
title_auth	Deep learning-assisted PET imaging achieves fast scan/low-dose examination
abstract	Purpose This study aimed to investigate the impact of a deep learning (DL)-based denoising method on the image quality and lesion detectability of 18F-FDG positron emission tomography (PET) images. Methods Fifty-two oncological patients undergoing an 18F-FDG PET/CT imaging with an acquisition of 180 s per bed position were retrospectively included. The list-mode data were rebinned into four datasets: 100% (reference), 75%, 50%, and 33.3% of the total counts, and then reconstructed by OSEM algorithm and post-processed with the DL and Gaussian filter (GS). The image quality was assessed using a 5-point Likert scale, and FDG-avid lesions were counted to measure lesion detectability. Standardized uptake values (SUVs) in livers and lesions, liver signal-to-noise ratio (SNR) and target-to-background ratio (TBR) values were compared between the methods. Subgroup analyses compared TBRs after categorizing lesions based on parameters like lesion diameter, uptake or patient habitus. Results The DL method showed superior performance regarding image noise and inferior performance regarding lesion contrast in the qualitative assessment. More than 96.8% of the lesions were successfully identified in DL images. Excellent agreements on SUV in livers and lesions were found. The DL method significantly improved the liver SNR for count reduction down to 33.3% (p < 0.001). Lesion TBR was not significantly different between DL and reference images of the 75% dataset; furthermore, there was no significant difference either for lesions of > 10 mm or lesions in BMIs of > 25. For the 50% dataset, there was no significant difference between DL and reference images for TBR of lesion with > 15 mm or higher uptake than liver. Conclusions The developed DL method improved both liver SNR and lesion TBR indicating better image quality and lesion conspicuousness compared to GS method. Compared with the reference, it showed non-inferior image quality with reduced counts by 25–50% under various conditions. © The Author(s) 2022
abstractGer	Purpose This study aimed to investigate the impact of a deep learning (DL)-based denoising method on the image quality and lesion detectability of 18F-FDG positron emission tomography (PET) images. Methods Fifty-two oncological patients undergoing an 18F-FDG PET/CT imaging with an acquisition of 180 s per bed position were retrospectively included. The list-mode data were rebinned into four datasets: 100% (reference), 75%, 50%, and 33.3% of the total counts, and then reconstructed by OSEM algorithm and post-processed with the DL and Gaussian filter (GS). The image quality was assessed using a 5-point Likert scale, and FDG-avid lesions were counted to measure lesion detectability. Standardized uptake values (SUVs) in livers and lesions, liver signal-to-noise ratio (SNR) and target-to-background ratio (TBR) values were compared between the methods. Subgroup analyses compared TBRs after categorizing lesions based on parameters like lesion diameter, uptake or patient habitus. Results The DL method showed superior performance regarding image noise and inferior performance regarding lesion contrast in the qualitative assessment. More than 96.8% of the lesions were successfully identified in DL images. Excellent agreements on SUV in livers and lesions were found. The DL method significantly improved the liver SNR for count reduction down to 33.3% (p < 0.001). Lesion TBR was not significantly different between DL and reference images of the 75% dataset; furthermore, there was no significant difference either for lesions of > 10 mm or lesions in BMIs of > 25. For the 50% dataset, there was no significant difference between DL and reference images for TBR of lesion with > 15 mm or higher uptake than liver. Conclusions The developed DL method improved both liver SNR and lesion TBR indicating better image quality and lesion conspicuousness compared to GS method. Compared with the reference, it showed non-inferior image quality with reduced counts by 25–50% under various conditions. © The Author(s) 2022
abstract_unstemmed	Purpose This study aimed to investigate the impact of a deep learning (DL)-based denoising method on the image quality and lesion detectability of 18F-FDG positron emission tomography (PET) images. Methods Fifty-two oncological patients undergoing an 18F-FDG PET/CT imaging with an acquisition of 180 s per bed position were retrospectively included. The list-mode data were rebinned into four datasets: 100% (reference), 75%, 50%, and 33.3% of the total counts, and then reconstructed by OSEM algorithm and post-processed with the DL and Gaussian filter (GS). The image quality was assessed using a 5-point Likert scale, and FDG-avid lesions were counted to measure lesion detectability. Standardized uptake values (SUVs) in livers and lesions, liver signal-to-noise ratio (SNR) and target-to-background ratio (TBR) values were compared between the methods. Subgroup analyses compared TBRs after categorizing lesions based on parameters like lesion diameter, uptake or patient habitus. Results The DL method showed superior performance regarding image noise and inferior performance regarding lesion contrast in the qualitative assessment. More than 96.8% of the lesions were successfully identified in DL images. Excellent agreements on SUV in livers and lesions were found. The DL method significantly improved the liver SNR for count reduction down to 33.3% (p < 0.001). Lesion TBR was not significantly different between DL and reference images of the 75% dataset; furthermore, there was no significant difference either for lesions of > 10 mm or lesions in BMIs of > 25. For the 50% dataset, there was no significant difference between DL and reference images for TBR of lesion with > 15 mm or higher uptake than liver. Conclusions The developed DL method improved both liver SNR and lesion TBR indicating better image quality and lesion conspicuousness compared to GS method. Compared with the reference, it showed non-inferior image quality with reduced counts by 25–50% under various conditions. © The Author(s) 2022
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container_issue	1
title_short	Deep learning-assisted PET imaging achieves fast scan/low-dose examination
url	https://dx.doi.org/10.1186/s40658-022-00431-9
remote_bool	true
author2	Qiao, Wenli Wang, Taisong Wang, Ying Li, Chenwei Lv, Yang Xi, Chen Liao, Shu Qian, Zheng Zhao, Jinhua
author2Str	Qiao, Wenli Wang, Taisong Wang, Ying Li, Chenwei Lv, Yang Xi, Chen Liao, Shu Qian, Zheng Zhao, Jinhua
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doi_str	10.1186/s40658-022-00431-9
up_date	2024-07-03T20:44:59.706Z
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